What’s New in hana.ml.r v2.10.210918

New Functions:

  • TextMining : TF.Analysis, Text.Classification, Get.Related.Doc, Get.Relevant.Doc, Get.Related.Term, Get.Relevant.Term, Get.Suggested.Term, Text.Collector, Text.TFIDF.
  • New function hanaml.OnlineLinearRegression(), an online version of linear regression.
  • New function hanaml.OnlineMultiLogisticRegression(), an online version of multi logistic regression.
  • New Bayesian Change-Point Detection function hanaml.BCPD().

Enhancement: - Categorical feature support in AdditiveModelForecast(). - Supports SAP HANA Data Lake in ModelStorage.

API Changes:

  • Added a new parameter categorical.variable in hanaml.AdditiveModelForecast().

What’s New in hana.ml.r v2.9.210619

Bug Fixed:

  • Fixed missing default value of parameter ‘cols’ in hanaml.CovarianceMatrix and hanaml.PearsonrMatrix.
  • Fixed missing schema setting issue in hanaml.ConvertToHANADataFrame using JDBC methods to write the HANA table.
  • Fixed lowercase error of ACCURACY_MEASURE spelling.
  • Fixed the corrected the value of param intercept in linear regression.

What’s New in hana.ml.r v2.8.210321

Version 2.8.210321 supports SAP HANA SPS05 and SAP HANA Cloud

New Functions:

  • Added tbl() to support dbplyr
  • Added hana2dbplyr() and dbplyr2hana() to subpport dbplyr
  • New Unified functions: hanaml.UnifiedRegression, hanaml.UnifiedClustering.
  • New Markov Chain Monte Carlo function: hanaml.mcmc.
  • New ‘hana.version’ function is provided by hanaml.ConnectionContext.
  • New function hanaml.OnlineARIMA.
  • New function hanaml.VectorARIMA.
  • New function hanaml.sqltrace.

API Changes:

  • hanaml.KMeans with two added parameters ‘use.fast.library’ and ‘use.float’.
  • Added a parameter ‘distance.level’ in hanaml.UnifiedClustering when ‘func’ is AgglomerateHierarchicalClustering and DBSCAN. Please refer to documentation for details.
  • Added a parameter ‘range.penalty’ in hanaml.CPD.
  • Provides more methods of ‘decomposition’ in various regression functions, please refer to the documentation of the specific algorithm.
  • Added a parameter ‘key’ in prediction() function of hanaml.ARIMA and hanaml.AutoARIMA.
  • Added 2 parameters ‘min.measure’, ‘max.consequent’ in hanaml.KORD.
  • Added a parameter ‘output.threshold’ in hanaml.AUC to enable the output of threshold values in roc table.
  • Removed RODBC dependency.

Enhancement:

  • Improved the data upload in ConvertToHANADataFrame() for odbc and jdbc connection.
  • Enhanced hanaml.UnifiedClustering to support ‘distance.level’ in AgglomerateHierarchicalClustering and DBSCAN functions. Please refer to the documentation for details.
  • Enhanced the speed of data import functionality when odbc is applied.
  • Enhanced the validation of types of parameters.
  • Enhanced the parameter check of season.start and allow.linear in hanaml.AutoARIMA.
  • Enhanced the model storage for hanaml.ARIMA, hanaml.AutoARIMA, hanaml.VectorARIMA and hanaml.OnlineARIMA.

Bug Fixed:

  • Fixed the displacement of parameter ‘dispersion’ in hanaml.CPD.
  • Fixed the displacement of parameter ‘category.weight’ in hanaml.GaussianMixture.

What’s New in hana.ml.r v2.6.201016

API Changes:

  • HybridGradientBoostingClassifier, HybridGradientBoostingRegressor: added a parameter ‘adopt_prior’ to indicate whether to adopt the prior distribution as the initial point.
  • LinearRegression: added a parameter ‘features.must.select’ to specifies the column name that needs to be included in the final training model when executing the variable selection.

What’s New in hana.ml.r v2.6.200928

API Changes:

  • Added encrypt, validateCertificate and autocommit options to JDBC ConnectionContext.
  • SVC, SVR, OneClassSVM, SVRanking: added parameters ‘compression’, ‘max.bits’, ‘max.quantization.iter’ for model compression.
  • RandomForestClassifier: added parameters ‘compression’, ‘max.bits’, ‘quantize.rate’ for model compression.
  • RandomForestRegressor: added parameters ‘compression’, ‘max.bits’, ‘quantize.rate’, ‘fittings.quantization’ for model compression.
  • In prediction function ARIMA and AutoARIMA, new value ‘truncation.algorithm’ of forecast_method is introduced to improve the prediction performance.
  • New parameters ‘string.variable’, ‘variable.weight’ added in KNNClassifier, KNNRegressor and DBSCAN to enable distance calculation based on String distance.
  • New parameters ‘extrapolation’, ‘smooth.width’, ‘auxiliary.normalitytest’ are added in SeasonalDecompose.

New Functions:

  • Clustering: SlightSilhouette.
  • Preprocessing : SMOTETomek, TomekLinks.

Bug Fixed:

  • Fixed version check to support HANA cloud.
  • Fixed the error in random distribution sampling when full distribution parameter set is specified.
  • Fixed HAS_ID error in TomekLinks.
  • Fixed the Collect() method that returns ‘No Data’ when contraining a column of type ST_GEOMETRY(in particular, for GeoDBSCAN).

What’s New in hana.ml.r v2.5.200626

API Changes:

  • Removed parameter ConnectionContext in functions.
  • Added a new parameter ‘decay’ to replace ‘learning.rate’ as the later one is misleading in SOM().
  • Added a new parameter ‘stratified.columns’ in hanaml.Sampling() to replace ‘features’ whose name is misleading.
  • Added a new parameter ‘col.types’ in ConvertToHANADataFrame() to replace ‘clob.columns’ for enhancement.
  • Changed parameter ‘seed’ to ‘random.state’ for parameter name consistency in MLPClassifier() and MLPRegressor().
  • Update function names for consistency: Arima -> ARIMA, AutoArima -> AutoARIMA， Auc -> AUC, Kmeans -> KMeans, Kmedian -> KMedian, Kmedoid -> KMedoid, Kord -> KORD.

New Functions:

  • Recommender System Algorithms : Alternating Least Square, Factorized Polynomial Regression Models, Field-Aware Factorization Machine.
  • Regression : Cox Proportional Hazard Model.
  • Statistics Functions : Cumulative Distribution Function (CDF), Distribution Fitting, Distribution Quantile, Entropy, Equal Variance Test, Factor Analysis, Wilcoxon Signed Rank Test, Grubbs’ Test, Kaplan-Meier Survival Analysis, Kernel Density Estimation, One-Sample Median Test.
  • Time Series : Fast DTW
  • Preprocessing : SMOTE
  • Miscellaneous : ABC Analysis, T-Distributed Stochastic Neighbour Embedding(TSNE), Weighted Score Table.
  • Unified classification
  • Model Selection

Bug Fixed:

  • Fixed parameter ‘formula’ parsing issue when a single feature is entered.
  • Fixed falsely recognizing the type of query statement with JDBC in sqlQueryMix().
  • Fixed phrasing error of parameter ‘timeout’ in Decision Tree, Linear Regression, Logistic Regression, Naive Bayes and SVM functions.

Enhancement:

  • Added cross-validation options to some functions (Decision Tree Classifier/Regressor, Gradient Boosting Classifier/Regressor, Hybrid Gradient Boosting Classifier/Regressor, Generalised Linear Models(GLM), Naive Bayes, Linear Regression, Logistic Regression Multi-Layer Perceptron Classifier/Regressor, Support Vector Machines functions, K-Nearest Neighbors Classifier/Regressor, Alternating Least Square(ALS), Factorized Polynomial Regression Models(FRM), Polynomial Regression).
  • Improved the robustness of ConvertToHANADataFrame().
  • Enhancement of ConvertToHANADataFrame() function: support data.frame input with missing (NA) values.

What’s New in hana.ml.r v1.0.8

Bug Fixed:

  • Fixed wrong error message with RJDBC connection. Add two additional error message for missing RJDBC connection and wrong RJDBC connection.context type.
  • Fix label type error in RandomForest. Add process to check continuous label type for regression and categorical label type for classification.
  • Fixed wrong API for DecisionTreeRegressor. Remove type conversion for result.
  • Fixed wrong cast error in Neighbors. Use NVARCHAR instead of DOUBLE for result.
  • Fixed wrong cast error in HGBT. Use NVARCHAR instead of DOUBLE for result.

What’s New in hana.ml.r v1.0.7

New Algorithms:

  • Association : Apriori, Apriorilite, FP-Growth, KORD, Sequential Pattern Mining (SPM)
  • Clustering : Affinity Propagation, Agglomerate Hierarchical Clustering, DBSCAN, Geometry DBSCAN, Latent Dirichlet Allocation, Self-Organizing Maps (SOM)
  • Classification : Conditional Random Field (CRF), Confusion Matrix, Hybrid Gradient Boosting Tree (HGBT), Logistic Regression, Multilayer Perceptron (MLP).
  • Regression : Bi-Variate Geometric Regression, Bi-Variate Natural Logarithmic Regression, Exponential Regression.
  • Time Series : ARIMA, Auto ARIMA, FFT, Seasonal Decompose, Trend Test, White Noise Test Single/Double/Triple/Auto/Brown Exponential Smoothing, Change-Point Detection, Croston’s Method, Linear Regression With Damped Trend And Seasonal Adjust, Additive Model Forecast, Hierarchical Forecast, Correlation Function.
  • Preprocessing : Discretize, Inter-Quartile Range (IQR), Missing Value Handing, Multidimensional Scaling (MDS), Partition, Random Distribution Sampling, Variance Test function.
  • Statistics Functions ： Chi-Squared Test Functions, T-Test Functions, Analysis Of Variance Functions (ANOVA), Univariate/Multivariate Analysis Functions.
  • Random Distribution Sampling Functions ： Bernoulli, Beta, Binomial, Cauchy, Chi_Squared, Exponential, Extreme_Value, F, Gamma, Geometric, Gumbel, Lognormal, Negative_Binomial, Normal, Pert, Poisson, Student_T, Uniform, Weibull, Multinomial).
  • Social Networks : Link Prediction, Pagerank.
  • Connection Context : JDBC Option.
  • Model Storage Services.
  • Dataframe Functions : ConvertToHANADataFrame.

Quality Improvements:

  • Use Anonymous Block.
  • Remove Unnecessary Temporary Table.
  • Error Message Enhancement.
  • Fix Documentation.
  • Code Quality Improvement.